摘要
在音频事件检测任务中,目标音频易受背景噪声等因素的干扰,并且其在音频信号流中存在的比例不高,针对这些问题,提出一种多层次注意力机制一维DenseNet(dense convolutional network)音频事件检测模型。使用一维DenseNet模型进行帧级检测能有效地检测音频事件发生的开始和结束时间;在一维DenseNet模型中引入多层次注意力机制,使得不同模块的感知特性随着网络层数的加深而自适应地变化,因此模型可以在不同的网络层次自动选择和关注重要的目标帧而抑制不相关的背景帧。在DCASE 2017任务2的开发数据集上的实验表明,该方法的整体性能较传统的深度学习方法有进一步提高。
In sound event detection tasks,the target event was susceptible to background noise,and wasn’t present in a significantly high portion of sound signal flow. To solve the problem,this paper proposed a new method of sound event detection based on one-dimensional dense convolutional network( DenseNet) with multi-level attention mechanism. Firstly,it used the one-dimensional DenseNet for frame-level detection,which was effective in finding the precise onset and offset time. Then,it introduced the multi-level attention mechanism in the one-dimensional DenseNet model,which made the attention-aware features from different modules change adaptively as layers went deeper. Therefore,the model could automatically select and attend on important frames for the targets while ignoring the unrelated parts( e. g. the background noise segments). Finally,this paper evaluated the model using DCASE 2017 Task 2 development dataset. Results show that the overall performance of the proposed method has further improvement than the conventional deep learning method.
作者
杨吕祥
胡燕
Yang Lyuxiang;Hu Yan(School of Computer Science&Technology,Wuhan University of Technology,Wuhan 430070,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第6期1642-1646,共5页
Application Research of Computers
基金
湖北省自然科学基金重点类资助项目(2017CFA012)。
